ERN from EF bias
Hannah, 3/9/2017
Data from previously processed EEG files from CU and MU were used. Before I got them, the following was done:
- CNT file was merged with dat file to add response latency and accuracy information to CNT file
- CNT was re-referenced to average mastoids reference
- Blinks were corrected for
- A filter was applied (need to look up the settings for that)
- Files were response-locked, with an epoch of -400 to 500
I subsequently:
- Baseline corrected EEG files (using a baseline of -400 to -200)
- Performed an automatic artifact rejected procedure (trials with +- 75 uV were rejected, only using 9 electrodes of interest as the criteria)
Trials were included if:
1) The RT was between 200 and 500 ms
2) A response was made (i.e., no miss trials)
3) The trial wasn’t rejected in artifact rejection procedure
The following subjects were excluded:
- 1040 (doesn’t have full number of trials)
- 2023 (problems with EEG data)
- 2077 (problems with EEG data)
- 2089 (problems with EEG data)
- 2151 (problems with EEG data)
- 2157 (problems with EEG data)
- 2181 (problems with EEG data)
- 2187 (doesn’t have full number of trials)
Each subject did 384 experimental trials (prime-only trials were also included but not in the 384).
FZ, F3, F4, FCZ, FC3, FC4, C3, CZ, C4 (9 electrodes) were included.
Total sample is 134 subjects, 60 from CU and 74 from MU.
1. ERN/CRN grand averages
Negative is plotted upward.
To test the mean amplitude of the ERNs, a model was fitted with Race and Object as predictors. The intercept, slopes of Race and Object, and their interaction were allowed to vary by subject. The intercept was allowed to vary by Electrode nested within Subject.
Race and Object were both effect coded.
Random effects:
## Groups Name Std.Dev. Corr
## Electrode:Subject (Intercept) 1.63488
## Subject (Intercept) 2.81651
## Race.e 0.95942 -0.108
## Object.e 1.25933 -0.021 -0.002
## Race.e:Object.e 0.88610 -0.009 -0.198 -0.049
## Residual 7.56401Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.165 0.250 132.629 -4.664 0.000
## Race.e 0.638 0.088 128.279 7.248 0.000
## Object.e -0.518 0.113 129.698 -4.591 0.000
## Race.e:Object.e 0.218 0.082 124.091 2.652 0.0092. Looking at the ERN over the course of the experiment
- Each error occurs at original trial number
Slopes and estimates of lines are from the MLM, not fitted with OLS. Negative is plotted downward.
Simple slopes
Trial is scaled to range from 0 to 10 (instead of 1 to 384) so that the betas associated with trial are a little bigger (but significance testing is unaffected by linear scaling, so the test statistics and p values will be the same as if we used the unscaled Trial variable).
## Estimate SE ci95_lower ci95_upper Race Object Color
## 1 0.000772 0.00210 -0.00342 0.00497 Black gun light blue
## 2 0.011084 0.00167 0.00775 0.01442 Black tool dark blue
## 3 0.008099 0.00180 0.00449 0.01170 White gun light red
## 4 0.008638 0.00191 0.00482 0.01246 White tool dark redModel output
The intercept, slopes of current and previous trial condition and their interaction are allowed to vary by subject. Categorical variables are effect coded.
Trial is scaled to range from 0 to 10.
Random effects:
## Groups Name Std.Dev. Corr
## Electrode:Subject (Intercept) 1.63511
## Subject (Intercept) 2.81624
## Race.e 0.95592 -0.103
## Object.e 1.25739 -0.022 -0.005
## Race.e:Object.e 0.88358 -0.003 -0.195 -0.057
## Residual 7.56052Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.542 0.255 143.528 -6.053 0
## Race.e 0.568 0.101 224.816 5.623 0
## Object.e -0.658 0.123 185.691 -5.333 0
## Race.e:Object.e 0.343 0.096 233.497 3.571 0## Estimate Std. Error df t value Pr(>|t|)
## Trial.begin 0.007 0.001 85974.57 7.619 0.000
## Race.e:Trial.begin 0.001 0.001 85911.09 1.301 0.193
## Object.e:Trial.begin 0.003 0.001 85989.96 2.892 0.004
## Race.e:Object.e:Trial.begin -0.002 0.001 85866.67 -2.605 0.0093. Looking at the ERN over the course of the experiment
- Each error is order for each subject, then centered for each subject
Slopes and estimates of lines are from the MLM, not fitted with OLS. Negative is plotted downward.
Simple slopes
## Estimate SE ci95_lower ci95_upper Race Object Color
## 1 0.00133 0.00237 -0.00340 0.00607 Black gun light blue
## 2 0.00932 0.00184 0.00565 0.01300 Black tool dark blue
## 3 0.00524 0.00201 0.00121 0.00927 White gun light red
## 4 0.00559 0.00211 0.00137 0.00981 White tool dark redModel output
The intercept, slopes of current and previous trial condition and their interaction are allowed to vary by subject. Categorical variables are effect coded.
For each subject, error trials are numbered (in order) and centered.
Random effects:
## Groups Name Std.Dev. Corr
## Electrode:Subject (Intercept) 1.63498
## Subject (Intercept) 2.81686
## Race.e 0.95784 -0.105
## Object.e 1.25814 -0.021 -0.003
## Race.e:Object.e 0.88589 -0.007 -0.197 -0.053
## Residual 7.56245Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.166 0.250 132.637 -4.667 0.00
## Race.e 0.636 0.088 128.313 7.229 0.00
## Object.e -0.517 0.113 129.695 -4.583 0.00
## Race.e:Object.e 0.215 0.082 124.182 2.624 0.01## Estimate Std. Error df t value Pr(>|t|)
## TrialOrder.c 0.005 0.001 85958.54 5.139 0.000
## Race.e:TrialOrder.c 0.000 0.001 85901.85 0.041 0.968
## Object.e:TrialOrder.c 0.002 0.001 85918.40 1.994 0.046
## Race.e:Object.e:TrialOrder.c -0.002 0.001 85887.99 -1.827 0.068